Making predictions

- [Instructor] Once you train the modeling keras,…you can put it to use.…Lets use a training model to make predictions for new data.…Lets open up predict.PY.…Here we've already loaded our data…and to find the neural network using keras sequential API.…Then on line 33,…we evaluated the model against the testing data set.…So this neural network is now trained…to look at characteristics of video games…and predict their future sales…based on those characteristics.…Lets try to make a prediction for a new video game.…

I've included a file called proposed new products.csv,…lets take a look.…This file has details about a hypothetical video game.…Lets use the neural network to predict…how much money this product will make.…Notice that the data here has already been pre-scaled…to the zero to one range just for convenience.…Alright, lets jump back to predict.py.…On line 37,…I've already loaded the proposed new product.csv file…using the same pans.csv function as we used before…and stored it in the variable called X.…

Now lets make a prediction for this data.…

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Released

8/1/2017

Keras is a popular programming framework for deep learning that simplifies the process of building deep learning applications. Instead of providing all the functionality itself, it uses either TensorFlow or Theano behind the scenes and adds a standard, simplified programming interface on top. In this course, learn how to install Keras and use it to build a simple deep learning model. Explore the many powerful pre-trained deep learning models included in Keras and how to use them. Discover how to deploy Keras models, and how to transfer data between Keras and TensorFlow so that you can take advantage of all the TensorFlow tools while using Keras. When you wrap up this course, you'll be ready to start building and deploying your own models with Keras.